Search ga_s2am_ard_3 whole collection

The proportion (from 0 to 100) of the dataset's valid data area that contains cloud pixels.\n\nFor these ARD products, this value comes from the Fmask algorithm.\n from:

The proportion (from 0 to 100) of the dataset's valid data area that contains cloud pixels.\n\nFor these ARD products, this value comes from the Fmask algorithm.\n to (exclusive):

Time when dataset was created (processed) From

Time when dataset was created (processed) to (exclusive)

The raw CRS string as it appears in metadata\n\n(e.g. ‘epsg:32654’)\n:

One of - final|interim|nrt (near real time):

ID of a dataset type:

Ground sampling distance of the sensor’s best resolution band\nin metres; represents the size (or spatial resolution) of one pixel.\n from:

Ground sampling distance of the sensor’s best resolution band\nin metres; represents the size (or spatial resolution) of one pixel.\n to (exclusive):

The azimuth angle of the sun at the moment of acquisition, in degree units measured clockwise from due north\n from:

The azimuth angle of the sun at the moment of acquisition, in degree units measured clockwise from due north\n to (exclusive):

The elevation angle of the sun at the moment of acquisition, in degree units relative to the horizon.\n from:

The elevation angle of the sun at the moment of acquisition, in degree units relative to the horizon.\n to (exclusive):

The proportion (from 0 to 100) of the dataset's valid data area that contains clear land pixels according to the Fmask algorithm\n from:

The proportion (from 0 to 100) of the dataset's valid data area that contains clear land pixels according to the Fmask algorithm\n to (exclusive):

The proportion (from 0 to 100) of the dataset's valid data area that contains cloud shadow pixels according to the Fmask algorithm\n from:

The proportion (from 0 to 100) of the dataset's valid data area that contains cloud shadow pixels according to the Fmask algorithm\n to (exclusive):

The proportion (from 0 to 100) of the dataset's valid data area that contains clear snow pixels according to the Fmask algorithm\n from:

The proportion (from 0 to 100) of the dataset's valid data area that contains clear snow pixels according to the Fmask algorithm\n to (exclusive):

The proportion (from 0 to 100) of the dataset's valid data area that contains clear water pixels according to the Fmask algorithm\n from:

The proportion (from 0 to 100) of the dataset's valid data area that contains clear water pixels according to the Fmask algorithm\n to (exclusive):

File format (GeoTiff, NetCDF):

Mean of the absolute values of the x-axis (east-to-west) GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres)\n from:

Mean of the absolute values of the x-axis (east-to-west) GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres)\n to (exclusive):

Mean of the absolute values of the GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres)\n from:

Mean of the absolute values of the GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres)\n to (exclusive):

Mean of the absolute values of the y-axis (north-to-south) GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres)\n from:

Mean of the absolute values of the y-axis (north-to-south) GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres)\n to (exclusive):

Absolute value of the x-axis (east-to-west) GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres)\n from:

Absolute value of the x-axis (east-to-west) GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres)\n to (exclusive):

Absolute value of the total GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres)\n from:

Absolute value of the total GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres)\n to (exclusive):

Absolute value of the y-axis (north-to-south) GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres)\n from:

Absolute value of the y-axis (north-to-south) GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres)\n to (exclusive):

Circular error probable (90%) of the values of the GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres)\n from:

Circular error probable (90%) of the values of the GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres)\n to (exclusive):

Mean of the values of the x-axis (east-to-west) GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres)\n from:

Mean of the values of the x-axis (east-to-west) GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres)\n to (exclusive):

Mean of the values of the GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres)\n from:

Mean of the values of the GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres)\n to (exclusive):

Mean of the values of the y-axis (north-to-south) GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres)\n from:

Mean of the values of the y-axis (north-to-south) GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres)\n to (exclusive):

Standard Deviation of the values of the x-axis (east-to-west) GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres)\n from:

Standard Deviation of the values of the x-axis (east-to-west) GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres)\n to (exclusive):

Standard Deviation of the values of the GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres)\n from:

Standard Deviation of the values of the GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres)\n to (exclusive):

Standard Deviation of the values of the y-axis (north-to-south) GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres)\n from:

Standard Deviation of the values of the y-axis (north-to-south) GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres)\n to (exclusive):

Mean of the values of the x-axis (east-to-west) GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres)\n from:

Mean of the values of the x-axis (east-to-west) GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres)\n to (exclusive):

Mean of the values of the GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres)\n from:

Mean of the values of the GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres)\n to (exclusive):

Mean of the values of the y-axis (north-to-south) GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres)\n from:

Mean of the values of the y-axis (north-to-south) GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres)\n to (exclusive):

Standard Deviation of the values of the x-axis (east-to-west) GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres)\n from:

Standard Deviation of the values of the x-axis (east-to-west) GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres)\n to (exclusive):

Standard Deviation of the values of the GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres)\n from:

Standard Deviation of the values of the GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres)\n to (exclusive):

Standard Deviation of the values of the y-axis (north-to-south) GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres)\n from:

Standard Deviation of the values of the y-axis (north-to-south) GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres)\n to (exclusive):

Dataset UUID:

User who indexed the dataset:

When dataset was indexed:

Label:

Latitude range from:

Latitude range to (exclusive):

Longitude range from:

Longitude range to (exclusive):

Full metadata document:

Metadata type name of dataset:

ID of a metadata type:

Spatial reference code from the provider.\nFor Sentinel it is MGRS code.\n:

The proportion (from 0 to 100) of the dataset's valid data area that contains clear land pixels according to s3cloudless\n from:

The proportion (from 0 to 100) of the dataset's valid data area that contains clear land pixels according to s3cloudless\n to (exclusive):

The proportion (from 0 to 100) of the dataset's valid data area that contains cloud land pixels according to s3cloudless\n from:

The proportion (from 0 to 100) of the dataset's valid data area that contains cloud land pixels according to s3cloudless\n to (exclusive):

Unique identifier for a datastrip relative to a given Datatake.\n\n(e.g. ‘S2A_OPER_MSI_L1C_DS_SGS__20161214T040601_S20161214T005840_N02.04’)\n:

ESA product URI, with the '.SAFE' ending removed.\n\n(e.g. 'S2A_MSIL1C_20220303T000731_N0400_R073_T56LNM_20220303T012845')\n:

Granule ID according to the ESA naming convention\n\n(e.g. ‘S2A_OPER_MSI_L1C_TL_SGS__20161214T040601_A007721_T53KRB_N02.04’)\n:

Acquisition time range From

Acquisition time range to (exclusive)

Dataset URI:

Add field

First 100 results (query limit)

Time (local)	Label
2015-10-28 11:59:52	ga_s2am_ard_3-2-1_49JGM_2015-10-28_final.stac-item
2016-05-28 12:14:47	ga_s2am_ard_3-2-1_49JFM_2016-05-28_final.stac-item
2016-10-22 12:02:45	ga_s2am_ard_3-2-1_49JGJ_2016-10-22_final.stac-item
2016-11-04 12:13:19	ga_s2am_ard_3-2-1_49JFM_2016-11-04_final.stac-item
2016-12-01 12:03:46	ga_s2am_ard_3-2-1_49JGN_2016-12-01_final.stac-item
2017-02-19 12:02:47	ga_s2am_ard_3-2-1_49JGL_2017-02-19_final.stac-item
2017-02-19 12:02:47	ga_s2am_ard_3-2-1_49JGM_2017-02-19_final.stac-item
2017-03-01 12:04:35	ga_s2am_ard_3-2-1_49JGL_2017-03-01_final.stac-item
2017-04-03 12:14:19	ga_s2am_ard_3-2-1_49JGM_2017-04-03_final.stac-item
2017-06-02 12:13:37	ga_s2am_ard_3-2-1_49JGM_2017-06-02_final.stac-item
2017-06-29 12:04:40	ga_s2am_ard_3-2-1_49JGK_2017-06-29_final.stac-item
2017-10-27 12:04:41	ga_s2am_ard_3-2-1_49JGL_2017-10-27_final.stac-item
2017-10-27 12:04:41	ga_s2am_ard_3-2-1_49JGN_2017-10-27_final.stac-item
2018-01-28 12:13:16	ga_s2am_ard_3-2-1_49JGM_2018-01-28_final.stac-item
2018-02-14 12:02:46	ga_s2am_ard_3-2-1_49JFM_2018-02-14_final.stac-item
2018-12-04 12:14:33	ga_s2am_ard_3-2-1_49JGN_2018-12-04_final.stac-item
2019-03-11 12:05:10	ga_s2am_ard_3-2-1_49JGL_2019-03-11_final.stac-item
2019-03-21 12:04:58	ga_s2am_ard_3-2-1_49JGM_2019-03-21_final.stac-item
2019-03-31 12:05:29	ga_s2am_ard_3-2-1_49JGK_2019-03-31_final.stac-item
2019-07-09 12:05:49	ga_s2am_ard_3-2-1_49JGJ_2019-07-09_final.stac-item
2019-07-22 12:14:46	ga_s2am_ard_3-2-1_49JGN_2019-07-22_final.stac-item
2019-09-30 12:15:12	ga_s2am_ard_3-2-1_49JGL_2019-09-30_final.stac-item
2019-10-10 12:14:59	ga_s2am_ard_3-2-1_49JGM_2019-10-10_final.stac-item
2019-10-30 12:14:58	ga_s2am_ard_3-2-1_49JGM_2019-10-30_final.stac-item
2019-12-06 12:05:27	ga_s2am_ard_3-2-1_49JGK_2019-12-06_final.stac-item
2020-01-15 12:04:40	ga_s2am_ard_3-2-1_49JGN_2020-01-15_final.stac-item
2020-02-14 12:05:22	ga_s2am_ard_3-2-1_49JGK_2020-02-14_final.stac-item
2020-02-27 12:14:51	ga_s2am_ard_3-2-1_49JGM_2020-02-27_final.stac-item
2020-03-05 12:05:25	ga_s2am_ard_3-2-1_49JGK_2020-03-05_final.stac-item
2020-04-07 12:14:53	ga_s2am_ard_3-2-1_49JGM_2020-04-07_final.stac-item
2020-05-27 12:14:47	ga_s2am_ard_3-2-1_49JGN_2020-05-27_final.stac-item
2020-06-06 12:15:06	ga_s2am_ard_3-2-1_49JFM_2020-06-06_final.stac-item
2020-12-03 12:14:55	ga_s2am_ard_3-2-1_49JGM_2020-12-03_final.stac-item
2021-03-10 12:05:43	ga_s2am_ard_3-2-1_49JGJ_2021-03-10_final.stac-item
2021-09-16 12:04:47	ga_s2am_ard_3-2-1_49JGN_2021-09-16_final.stac-item
2021-09-19 12:15:02	ga_s2am_ard_3-2-1_49JFM_2021-09-19_final.stac-item
2021-10-06 12:04:51	ga_s2am_ard_3-2-1_49JGN_2021-10-06_final.stac-item
2021-10-29 12:15:01	ga_s2am_ard_3-2-1_49JGM_2021-10-29_final.stac-item
2021-11-05 12:04:50	ga_s2am_ard_3-2-1_49JGN_2021-11-05_final.stac-item
2021-11-15 12:04:47	ga_s2am_ard_3-2-1_49JGN_2021-11-15_final.stac-item
2021-11-18 12:14:41	ga_s2am_ard_3-2-1_49JGN_2021-11-18_final.stac-item
2021-11-28 12:15:10	ga_s2am_ard_3-2-1_49JGL_2021-11-28_final.stac-item
2021-12-05 12:05:00	ga_s2am_ard_3-2-1_49JGM_2021-12-05_final.stac-item
2021-12-15 12:04:46	ga_s2am_ard_3-2-1_49JGN_2021-12-15_final.stac-item
2021-12-28 12:14:58	ga_s2am_ard_3-2-1_49JGM_2021-12-28_final.stac-item
2022-01-24 12:05:01	ga_s2am_ard_3-2-1_49JGM_2022-01-24_final.stac-item
2022-02-13 12:04:46	ga_s2am_ard_3-2-1_49JGN_2022-02-13_final.stac-item
2022-02-16 12:14:57	ga_s2am_ard_3-2-1_49JGM_2022-02-16_final.stac-item
2022-02-23 12:04:48	ga_s2am_ard_3-2-1_49JGN_2022-02-23_final.stac-item
2022-03-08 12:15:00	ga_s2am_ard_3-2-1_49JGM_2022-03-08_final.stac-item
2022-03-18 12:15:03	ga_s2am_ard_3-2-1_49JFM_2022-03-18_final.stac-item
2022-04-27 12:14:46	ga_s2am_ard_3-2-1_49JGN_2022-04-27_final.stac-item
2022-05-04 12:04:51	ga_s2am_ard_3-2-1_49JGN_2022-05-04_final.stac-item
2022-05-07 12:14:46	ga_s2am_ard_3-2-1_49JGN_2022-05-07_final.stac-item
2022-06-06 12:14:49	ga_s2am_ard_3-2-1_49JGN_2022-06-06_final.stac-item
2022-06-23 12:05:27	ga_s2am_ard_3-2-1_49JGL_2022-06-23_final.stac-item
2022-07-03 12:04:59	ga_s2am_ard_3-2-1_49JGN_2022-07-03_final.stac-item
2022-07-03 12:05:14	ga_s2am_ard_3-2-1_49JGM_2022-07-03_final.stac-item
2022-08-02 12:05:07	ga_s2am_ard_3-2-1_49JHM_2022-08-02_final.stac-item
2022-08-22 12:04:59	ga_s2am_ard_3-2-1_49JGN_2022-08-22_final.stac-item
2022-08-22 12:05:13	ga_s2am_ard_3-2-1_49JGM_2022-08-22_final.stac-item
2022-08-25 12:15:09	ga_s2am_ard_3-2-1_49JGM_2022-08-25_final
2022-09-01 12:05:27	ga_s2am_ard_3-2-1_49JGL_2022-09-01_final.stac-item
2022-09-11 12:05:25	ga_s2am_ard_3-2-1_49JGL_2022-09-11_final.stac-item
2022-11-03 12:15:14	ga_s2am_ard_3-2-1_49JGL_2022-11-03_final.stac-item
2022-11-13 12:14:44	ga_s2am_ard_3-2-1_49JGN_2022-11-13_final.stac-item
2023-01-09 12:04:44	ga_s2am_ard_3-2-1_49JGN_2023-01-09_final.stac-item
2023-01-29 12:04:58	ga_s2am_ard_3-2-1_49JGM_2023-01-29_final.stac-item
2023-02-08 12:04:45	ga_s2am_ard_3-2-1_49JGN_2023-02-08_final
2023-02-11 12:14:40	ga_s2am_ard_3-2-1_49JGN_2023-02-11_nrt.stac-item
2023-02-11 12:14:40	ga_s2am_ard_3-2-1_49JGN_2023-02-11_final
2023-02-11 12:14:54	ga_s2am_ard_3-2-1_49JGM_2023-02-11_nrt.stac-item
2023-02-11 12:14:54	ga_s2am_ard_3-2-1_49JGM_2023-02-11_final
2023-03-03 12:14:38	ga_s2am_ard_3-2-1_49JGN_2023-03-03_final
2023-04-09 12:05:15	ga_s2am_ard_3-2-1_49JGL_2023-04-09_final
2023-04-12 12:14:56	ga_s2am_ard_3-2-1_49JGM_2023-04-12_final
2023-05-09 12:05:00	ga_s2am_ard_3-2-1_49JGM_2023-05-09_final
2023-05-09 12:05:14	ga_s2am_ard_3-2-1_49JGL_2023-05-09_final
2023-05-12 12:14:40	ga_s2am_ard_3-2-1_49JGN_2023-05-12_final
2023-06-28 12:04:48	ga_s2am_ard_3-2-1_49JGN_2023-06-28_final
2023-07-01 12:14:46	ga_s2am_ard_3-2-1_49JGN_2023-07-01_final
2023-08-17 12:05:06	ga_s2am_ard_3-2-1_49JGM_2023-08-17_final
2023-09-16 12:05:04	ga_s2am_ard_3-2-1_49JGM_2023-09-16_final
2023-10-06 12:05:01	ga_s2am_ard_3-2-1_49JGM_2023-10-06_final
2023-10-09 12:14:41	ga_s2am_ard_3-2-1_49JGN_2023-10-09_final
2023-10-16 12:04:59	ga_s2am_ard_3-2-1_49JGM_2023-10-16_final
2023-10-19 12:14:56	ga_s2am_ard_3-2-1_49JGM_2023-10-19_final
2023-10-29 12:14:41	ga_s2am_ard_3-2-1_49JGN_2023-10-29_final
2023-10-29 12:14:56	ga_s2am_ard_3-2-1_49JGM_2023-10-29_final
2023-11-08 12:14:41	ga_s2am_ard_3-2-1_49JGN_2023-11-08_final
2023-11-08 12:14:55	ga_s2am_ard_3-2-1_49JGM_2023-11-08_final
2023-11-18 12:14:39	ga_s2am_ard_3-2-1_49JGN_2023-11-18_final
2023-11-18 12:14:54	ga_s2am_ard_3-2-1_49JGM_2023-11-18_final
2023-12-28 12:14:53	ga_s2am_ard_3-2-1_49JGM_2023-12-28_final
2024-01-17 12:15:02	ga_s2am_ard_3-2-1_49JGL_2024-01-17_final
2024-02-03 12:04:56	ga_s2am_ard_3-2-1_49JGM_2024-02-03_final
2024-02-13 12:04:43	ga_s2am_ard_3-2-1_49JGN_2024-02-13_final
2024-03-04 12:04:46	ga_s2am_ard_3-2-1_49JGN_2024-03-04_final
2024-03-24 12:05:02	ga_s2am_ard_3-2-1_49JGM_2024-03-24_final
2024-04-13 12:05:16	ga_s2am_ard_3-2-1_49JGL_2024-04-13_final

Return to ga_s2am_ard_3 whole collection overview

ga_s2am_ard_3: Geoscience Australia Sentinel 2A MSI Analysis Ready Data Collection 3

Baseline satellite data

Land and vegetation

Inland water

Sea, ocean and coast

Hazards

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